Assigning papers to reviewers is a large, long and difficult task for conference chairs and scientific committees. The reviewer assignment problem is a multi-agent problem which requires understanding reviewer expertise and paper topics for the matching process. This paper proposes to elaborate on variables used to compute reviewer expertise and aggregate multiple factors to find the fittest combination of reviewers to each paper. Expertise information is gathered implicitly from publicly available information and a reviewer profile is generated automatically. An OWA (Ordered Weighted Average) aggregation function is used to summarize information coming from different sources and rank the candidate reviewers for each paper. General constraints for the RAP (Reviewer Assignment Problem) have been incorporated into a real case example: (i) conflicts of interest between the reviewer and authors should be avoided, (ii) each paper must have a minimum number of reviewers, and (iii) each reviewer load cannot exceed a certain number of papers.